the broadcast storm problem in a mobile ad hoc...

Wireless Networks 8, 153–167, 2002 2002 Kluwer Academic Publishers. Manufactured in The Netherlands.

The Broadcast Storm Problem in a Mobile Ad Hoc Network

YU-CHEE TSENGDepartment of Computer Science and Information Engineering, National Chiao-Tung University, Hsin-Chu, 30050, Taiwan, R.O.C.

SZE-YAO NIDepartment of Computer Science and Information Engineering, National Central University, Chung-Li, 32054, Taiwan, R.O.C.

YUH-SHYAN CHENDepartment of Statistics, National Taipei University, Taipei, 104, Taiwan, R.O.C.

JANG-PING SHEUDepartment of Computer Science and Information Engineering, National Central University, Chung-Li, 32054, Taiwan, R.O.C.

Abstract. Broadcasting is a common operation in a network to resolve many issues. In a mobile ad hoc network (MANET) in particular,due to host mobility, such operations are expected to be executed more frequently (such as finding a route to a particular host, paging aparticular host, and sending an alarm signal). Because radio signals are likely to overlap with others in a geographical area, a straightforwardbroadcasting by flooding is usually very costly and will result in serious redundancy, contention, and collision, to which we call the broadcaststorm problem. In this paper, we identify this problem by showing how serious it is through analyses and simulations. We propose severalschemes to reduce redundant rebroadcasts and differentiate timing of rebroadcasts to alleviate this problem. Simulation results are presented,which show different levels of improvement over the basic flooding approach.

Keywords: broadcast, communication, mobile ad hoc network (MANET), mobile computing, wireless network

1. Introduction

The advancements in wireless communication and economi-cal, portable computing devices have made mobile comput-ing possible. One research issue that has attracted a lot ofattention recently is the design of mobile ad hoc networks(MANET). A MANET is one consisting of a set of mobilehosts which may communicate with one another and roamaround at their will. No base stations are supported in suchan environment. Due to considerations such as radio powerlimitation, channel utilization, and power-saving concerns, amobile host may not be able to communicate directly withother hosts in a single-hop fashion. In this case, a multihopscenario occurs, where the packets sent by the source hostare relayed by several intermediate hosts before reaching thedestination host.

Applications of MANETs occur in situations like battle-fields or major disaster areas where networks need to be de-ployed immediately but base stations or fixed network in-frastructures are not available. Unicast routing in MANEThas been studied in several articles [7,8,15,16,23,25]. A work-ing group called “manet” has been formed by the Internet En-gineering Task Force (IETF) to study the related issues andstimulate research in MANET [22].

This paper studies the problem of sending a broadcastmessage in a MANET. Broadcasting is a common operationin many applications, e.g., graph-related problems and dis-tributed computing problems. It is also widely used to re-solve many network layer problems. In a MANET in par-

ticular, due to host mobility, broadcasting is expected to beperformed more frequently (e.g., for paging a particular host,sending an alarm signal, and finding a route to a particularhost [7,15,16,25]). Broadcasting may also be used in LANemulation [3] or serve as a last resort to provide multicast ser-vices in networks whose topologies change rapidly.

In this paper, we assume that mobile hosts in the MANETshare a single common channel with carrier sense multipleaccess (CSMA), but no collision detection (CD), capability.Synchronization in such a network with mobility is unlikely,and global network topology information is unavailable to fa-cilitate the scheduling of a broadcast. So one straightforwardand obvious solution is broadcasting by flooding. Unfortu-nately, in this paper we observe that serious redundancy, con-tention, and collision could exist if flooding is done blindly.First, because the radio propagation is omni-directional and aphysical location may be covered by the transmission rangesof several hosts, many rebroadcasts will be redundant. Sec-ond, heavy contention could exist because rebroadcastinghosts are probably close to each other. Third, collisions aremore likely to occur because the RTS/CTS dialogue is inap-plicable and the timing of rebroadcasts is highly correlated.

Collectively, we refer to these problems associated withflooding as the broadcast storm problem. Through analy-ses and simulations, we demonstrate how serious the prob-lem is. Two directions to alleviate this problem are to reducethe possibility of redundant rebroadcasts and to differentiatethe timing of rebroadcasts. Following these directions, wedevelop several schemes, called probabilistic, counter-based,

154 TSENG ET AL.

distance-based, location-based, and cluster-based schemes,to facilitate MANET broadcasting. Simulation results are pre-sented to study the effectiveness of these schemes.

To the best of our knowledge, the broadcast storm problemhas not been addressed in depth for MANET before. It is how-ever worth summarizing some results for broadcasting thatare for other environments. Works in [4,5,10–12,21] assumea packet-radio network environment. Most of these resultsrely on time division multiple access (TDMA, which requirestiming synchronization) and certain levels of topology infor-mation. Their goal is to find a slot assignment. Obtainingan optimal assignment has been shown to be NP-hard [10].The broadcast scheduling problem studied in [9,17,26,27],although carrying a similar name, is not intended to solvethe problem addressed in this paper. Its goal is to assign acontention-free time slot to each radio station.

The rest of this paper is organized as follows. Section 2 de-fines and analyzes the broadcast storm problem. Mechanismsto alleviate the storm are proposed in section 3. Simulationresults are presented in section 4 and conclusions are drawnin section 5.

2. Preliminaries

2.1. Broadcasting in a MANET

A MANET consists of a set of mobile hosts that may com-municate with one another from time to time. No base sta-tions are supported. Each host is equipped with a CSMA/CA(carrier sense multiple access with collision avoidance) [20]transceiver. In such environment, a host may communicatewith another directly or indirectly. In the latter case, a multi-hop scenario occurs, where the packets originating from thesource host are relayed by several intermediate hosts beforereaching the destination.

The broadcast problem refers to the sending of a messageto other hosts in the network. The problem considered here isassumed to have the following characteristics.

• The broadcast is spontaneous. Any mobile host can issuea broadcast message at any time. For reasons such as hostmobility and lack of synchronization, preparing any kindof global topology knowledge is prohibitive (in fact this isat least as hard as the broadcast problem). Little or evenno local connectivity information may be collected in ad-vance.

• The broadcast is unreliable.1 No acknowledgement mech-anism will be used.2 However, an attempt should be madeto distribute a broadcast message to as many hosts as pos-sible without paying too much effort. The motivations to

1 A more strict one is reliable broadcast [1,24], whose goal is to ensure allhosts receive the message. High-level acknowledgements between hostsare exchanged. Such protocols are typically accomplished at the applica-tion layer and are out of the scope of this paper. However, the result in thispaper may serve as an underlying facility to implement reliable broadcast.

2 The MAC specification in IEEE 802.11 [20] does not require acknowledge-ment on receipt of broadcast packets.

make such an assumption are (i) a host may miss a broad-cast message because it is off-line, it is temporarily iso-lated from the network, or it experiences repetitive colli-sions, (ii) acknowledgements may cause serious mediumcontention (and thus, another “storm”) surrounding thesender, and (iii) in many applications (e.g., route discoveryin [7,15,16,25]), a 100% reliable broadcast is unnecessary.

In addition, we assume that a host can detect duplicatebroadcast messages. This is essential to prevent endless flood-ing of a message. One way to do so is to associate with eachbroadcast message a tuple (source ID, sequence number) asthat in [7,25].

Finally, we comment that we do not confine ourselvesto the broadcasting of the same message.3 What we fo-cus on in this paper is the message propagating behavior ina MANET – the phenomenon where the transmission of apacket will trigger other surrounding hosts to transmit thesame (or modified) packet. We shall show that if floodingis used blindly, many redundant messages will be sent andserious contention/collision will be incurred. Our goal is tosolve broadcast with efficiency in mind.

2.2. Broadcast storm caused by flooding

A straight-forward approach to perform broadcast is by flood-ing. A host, on receiving a broadcast message for the firsttime, has the obligation to rebroadcast the message. Clearly,this costs n transmissions in a network of n hosts. In aCSMA/CA network, drawbacks of flooding include:

• Redundant rebroadcasts. When a mobile host decides torebroadcast a broadcast message to its neighbors, all itsneighbors already have the message.

• Contention. After a mobile host broadcasts a message, ifmany of its neighbors decide to rebroadcast the message,these transmissions (which are all from nearby hosts) mayseverely contend with each other.

• Collision. Because of the deficiency of backoff mecha-nism, the lack of RTS/CTS dialogue, and the absence ofCD, collisions are more likely to occur and cause moredamage.

Collectively, we refer to the above phenomena as the broad-cast storm problem. The following discussion shows how se-rious the problem is through analyses.

2.2.1. Analysis of redundant rebroadcastsWe first use two examples to demonstrate how much redun-dancy could be generated. In figure 1(a), it only takes twotransmissions for the white node to broadcast a message,whereas four transmissions will be carried out if no attemptis made to reduce redundancy. Figure 1(b) shows an even

3 For instance, the routing protocols in [7,15,16,25] rely on broadcasting aUDP packet called route_request to search for a route from a source to aparticular destination. When propagating such a request, a host generallyappends its ID to the message so that appropriate routing information canbe collected.

THE BROADCAST STORM PROBLEM IN A MOBILE AD HOC NETWORK 155

Figure 1. Two optimal broadcasting schedules in MANETs. Connectivitybetween hosts is represented by links. White nodes are source hosts, and

gray nodes are relay hosts.

Figure 2. The signal overlapping problem corresponding to the scenario infigure 1(b).

more serious scenario: only two transmissions are sufficientto complete a broadcast as opposed to seven transmissionscaused by flooding.

The main reason for such redundancy is that radio sig-nals from different antennas are very likely to overlap witheach other. Assuming that the area that can be covered by anantenna forms a circle, we show in figure 2 the signal over-lapping problem corresponding to the scenario in figure 1(b).The gray levels in the figure indicate the levels of signal over-lapping. As can be seen, many areas are covered by the samebroadcast packet more than once. In the worst case, an areacan be covered by the packet seven times.

In the following we will show through analyses that re-broadcasts are very costly and should be used with caution.First we consider the simple scenario in figure 3, where host Asends a broadcast message, and host B decides to rebroad-cast the message. Let SA and SB denote the circle areascovered by A’s and B’s transmissions, respectively. Theadditional area that can benefit from B’s rebroadcast is theshaded region, denoted as SB−A. Let r be the radii of SAand SB, and d the distance between A and B. We can derive

Figure 3. Analysis of the extra area that can benefit from a rebroadcast:A sends a broadcast packet and B decides to rebroadcasts the packet.

|SB−A| = |SB|−|SA∩B| = πr2−INTC(d), where INTC(d) isthe intersection area of the two circles centered at two pointsdistanced by d ,

INTC(d) = 4∫ r

d/2

√r2 − x2 dx.

When d = r , the coverage area |SB−A| is the largest, whichequals

πr2 − INTC(r) = r2(

π

3+

√3

2

)≈ 0.61πr2.

This shows a surprising fact that a rebroadcast can provideonly 0–61% additional coverage over that already covered bythe previous transmission.

Also, we would like to know the average value of πr2 −INTC(d). Suppose that B can randomly locate in any of A’stransmission range; the average value can be obtained by in-tegrating the above value over the circle of radius x centeredat A for x in [0, r]:

∫ r

0

2πx · [πr2 − INTC(x)]πr2 dx ≈ 0.41πr2.

Thus, after the previous broadcast, a rebroadcast can coveronly additional 41% area on average.

Now consider the scenario of having received a broadcastmessage twice: if host C decides to rebroadcast after it heardA’s and B’s broadcasts. The area that can benefit from C’srebroadcast is SC−(A∪B). Through simulations by randomlygenerating A and B on C’s transmission range, we found thaton average |SC−(A∪B)| ≈ 0.19πr2 (we remark that in the sim-ulations the calculation of coverage was not derived by formalcalculus, but by discrete estimation, where the area was parti-tioned into fine grids for approximation). This shows an evendimmer prospect of hoping that rebroadcasts propagate themessage to new hosts.

In general, we would like to know the benefit of a host re-broadcasting a message after hearing the message k times.The result can be easily obtained from simulation by ran-domly generating k hosts in a host X’s transmission rangeand calculating the area covered by X excluding those areasalready covered by the other k hosts. Denote this value byEAC(k) (EAC stands for expected additional coverage). Fig-ure 4 shows our simulation result (again by grid estimation).As can be seen, when k � 4, the expected additional coverageis below 5%.

156 TSENG ET AL.

Figure 4. Analysis on redundancy: the expected additional coverage EAC(k)

(divided by πr2) after a host heard a broadcast message k times.

2.2.2. Analysis of contentionTo address the contention problem, consider the situationwhere host A transmits a broadcast message and there aren hosts hearing this message. If all these hosts try to rebroad-cast the message, contention may occur because two or morehosts around A are likely to be close and, thus, contend witheach other on the wireless medium.

Let us analyze the simpler case of n = 2. Let hosts B andC be the two receiving hosts. Let B randomly locate withinA’s transmission range. In order for C to contend with B,it must be located in the area SA∩B. So the probability ofcontention is |SA∩B|/(πr2). Let x be the distance betweenA and B. Integrating the above probability over the circle ofradius x centered at A for x in [0, r], the expected probabilityof contention is

∫ r

0

2πx · INTC(x)/(πr2)

πr2dx ≈ 59%.

Clearly, the contention is expected to be higher as n in-creases. We derived a simulation by randomly generating n

hosts in A’s transmission range. We observe the probabilitycf (n, k) that k hosts among these n hosts experience no con-tention in their rebroadcasting (cf stands for contention-free).The results are shown in figure 5. We can see that the prob-ability of all n hosts experiencing contention (i.e., cf (n, 0))increases quickly over 0.8 as n � 6. So the more crowdedthe area is, the more serious the contention is. On the otherhand, the probability of having one contention-free host (i.e.,cf (n, 1)) drops sharply as n increases. Further, it is very un-likely to have more contention-free hosts (i.e., cf (n, k) withk � 2). Note that having k = n − 1 contention-free hostsimplies having n such hosts, so cf (n, n − 1) = 0.

2.2.3. Analysis of collisionIn a MANET, there is no base station or access point. There-fore, in this paper we exclude the use of the point coordinatefunction (PCF) described in the IEEE 802.11 MAC specifica-tion [20], and study mainly the behavior under the distributedcoordinate function (DCF).

Figure 5. Analysis of contention: the probabilities of having k contention-free hosts among n receiving hosts.

The CSMA/CA mechanism requires a host to start a back-off procedure right after the host transmitted a message, orwhen a host wants to transmit but the medium is busy andthe previous backoff has been done. To perform a backoff, acounter is first set to an integer randomly picked from its cur-rent backoff window. If the channel clear assessment (CCA)mechanism of the host detects no channel activity during thepast slot (a fixed period), the counter is decreased by one.When the counter reaches zero, the backoff procedure is fin-ished.

Now consider the scenario where several neighbor hostshear a broadcast from host X. There are several reasons forcollisions to occur. First, if the surrounding medium of Xhas been quiet for enough long, all of X’s neighbors mayhave passed their backoff procedures. Thus, after hearing thebroadcast message (and having passed the DIFS period), theymay all start rebroadcasting at around the same time. Thisis especially true if carriers can not be sensed immediatelydue to such as RF delays and transmission latency. Second,because the RTS/CTS forewarning dialogue is not used in abroadcast transmission, the damage of collision is more seri-ous. Third, once collision occurs, without collision detection(CD), a host will keep transmitting the packet even if someof its foregoing bits have been garbled. And the longer thepacket is, the more the waste.

The above problem is not addressed in the ordinary IEEE802.11 MAC activities, possibly because the one-to-manycommunication behavior is not considered therein. For all theabove reasons, we believe that the broadcast storm problemdeserves serious study in a MANET environment.

3. Mechanisms to reduce redundancy, contention, andcollision

One approach to alleviating the broadcast storm problem is toinhibit some hosts from rebroadcasting to reduce the redun-


dancy, and thus, contention and collision. In the following, wepresent five schemes to do so. These schemes differ in howa mobile host estimates redundancy and how it accumulatesknowledge to assist in making its decision. Except the lastscheme, which relies on some local connectivity information,all schemes operate in a fully distributed manner.

3.1. Probabilistic scheme

An intuitive way to reduce rebroadcasts is to use probabilisticrebroadcasting. On receiving a broadcast message for the firsttime, a host will rebroadcast it with probability P . Clearly,when P = 1, this scheme is equivalent to flooding.

Note that to respond to the contention and collision prob-lems addressed in sections 2.2.2 and 2.2.3, we should insert asmall random delay (a number of slots) before rebroadcastingthe message, so the timing of rebroadcasting can be differen-tiated.

3.2. Counter-based scheme

When a host tries to rebroadcast a message, the rebroadcastmessage may be blocked by a busy medium, the backoff pro-cedure, and other queued messages. There is a chance for thehost to hear the same message again and again from other re-broadcasting hosts before the host actually starts transmittingthe message.

In section 2.2.1 we have shown that EAC(k), the expectedadditional coverage after hearing the message k times, is ex-pected to decrease quickly as k increases. We can prevent ahost from rebroadcasting when the expected additional cov-erage of the host’s rebroadcast becomes too low. This iswhat the counter-based scheme is based on. Specifically,a counter c is used to keep track of the number of times thebroadcast message is received. A counter threshold C is cho-sen. Whenever c � C, the rebroadcast is inhibited. Thescheme is formally derived below.

S1. Initialize counter c = 1 when a broadcast message msgis heard for the first time. In S2, if msg is heard again,interrupt the waiting and perform S4.

S2. Wait for a random number of slots. Then submit msgfor transmission and wait until the transmission actuallystarts.

S3. The message is on the air. The procedure exits.

S4. Increase c by one. If c < C, resume the interrupted wait-ing in S2. Otherwise c = C, proceed to S5.

S5. Cancel the transmission of msg if it was submitted in S2.The host is prohibited from rebroadcasting the same mes-sage in the future. Then the procedure exits.

Note that in S4, by “resume the interrupted waiting in S2”,we mean that the host should go back to step S2 and wait forthe remaining amount of time that it should have done afterthe point of interruption.

3.3. Distance-based scheme

In the previous scheme, a counter is used to decide whetherto drop a rebroadcast or not. In this scheme, we will use therelative distance between hosts to make the decision.

For instance, suppose host H heard a broadcast messagefrom S for the first time. If the distance, say d , between H andS is very small, there is little additional coverage H’s rebroad-cast can provide. If d is larger, the additional coverage willbe larger. In the extreme case, if d = 0, the additional cov-erage is 0 too. Earlier, we analyzed the relationship betweenthe distance d and the additional coverage πr2 − INTC(d).So this can be used as a metric by H to determine whether torebroadcast or not.

Now, suppose that before a rebroadcast message is actu-ally sent, host H has heard the same message several times.Let dmin be the distance to the nearest host from which thesame message is heard. Then H’s rebroadcast will provideadditional coverage no more than πr2 − INTC(dmin). In ourdistance-based scheme, we will use dmin as the metric to eval-uate whether to rebroadcast or not. If dmin is smaller thansome distance threshold D, the rebroadcast transmission ofH is cancelled. The scheme is formally derived below. Insection 4, we will test several possible values of D.

S1. When a broadcast message msg is heard for the first time,initialize dmin to the distance to the broadcasting host. Ifdmin < D, proceed to S5. In S2, if msg is heard again,interrupt the waiting and perform S4.



S4. Update dmin if the distance to the host from which msg isheard is smaller. If dmin < D, proceed to S5. Otherwise,resume the interrupted waiting in S2.


Below, we comment on how to obtain the distance infor-mation. One possibility is to estimate from the signal strengthof a received message. Specifically, let Pt and Pr be the powerlevels at which a message is sent and received, respectively.According to [28], Pr = Pt(c1/d)nc2, where n, c1, and c2are constants related to the physical environment, the carrier’swavelength, and antenna gains, respectively. Since Pr and Pt

can be measured, the distance d can be estimated from thisformula.

Having understood the relationship between the distanceand the power, we can even directly replace the role of dis-tances by signal strengths by establishing a signal-strengththreshold. As a comment, we note that signal strength infor-mation was also used in [13] to facilitate routing in a MANET.

158 TSENG ET AL.

Figure 6. Scenarios of using convex polygons to determine whether to rebroadcast or not. (a) Host X is inside the triangle formed by three sending hosts.(b) X is outside of the polygon. (c) Analysis of maximum loss of additional coverage on using the polygon test.

3.4. Location-based scheme

Earlier we have used the number of times that a broadcastmessage has been heard or the distances to sending hosts asour rebroadcasting metrics. If we can acquire the locationsof those broadcasting hosts, it is even possible to estimate theadditional coverage more precisely. Such an approach may besupported by positioning devices such as GPS (Global Posi-tioning System) receivers [18]. We note that location informa-tion was also used to facilitate route discovery in a MANET[6,19].

Without loss of generality, let a host’s location be (0, 0)

(here we use xy-coordinates to facilitate our presentation; infact, devices such as GPS receivers can provide 3-D locationsin longitude, latitude, and altitude). Suppose a host has re-ceived the same broadcast message from k hosts located at(x1, y1), (x2, y2), . . . , (xk, yk). We can calculate the addi-tional area that can be covered provided that the host rebroad-casts the message. Let AC((x1, y1), (x2, y2), . . . , (xk, yk))

denote the additional coverage divided by πr2. Then wecan compare this value to a predefined coverage thresholdA (0 < A < 0.61) to determine whether the receiving hostshould rebroadcast or not. The scheme is formally derivedbelow.

S1. When a broadcast message msg is heard for the first time,initialize AC to the additional coverage provided by thehost’s rebroadcast. If AC < A, proceed to S5. In S2, ifmsg is heard again, interrupt the waiting and perform S4.



S4. Update AC. If AC < A, proceed to S5. Otherwise, re-sume the interrupted waiting in S2.


One obstacle to using the above scheme is the cost of cal-culating AC, which is related to calculating many intersectionareas among several circles. This problem is difficult already

when there are four circles. One possibility is to use a grid-filling approximation to estimate its value.

An alternative is using a convex polygon to determinewhether a rebroadcast should be carried out or not. For in-stance, suppose host X received a broadcast message threetimes from hosts A, B, and C. In figure 6(a), it shows thatif X is inside the convex polygon formed by connecting thecenters of A, B, and C, the additional coverage of X’s re-broadcast is small or even none. On the contrary, as shown infigure 6(b), if X is not in the polygon, it is likely that the re-broadcast will provide more additional coverage (the shadedarea). These observations suggest that we can allow the hostto rebroadcast only if it is not located within the convex poly-gon.

The following justifies the reason for the above convex ap-proximation through geometry calculation. We show that ifthe polygon test prevents a host X from rebroadcasting (X iswithin the polygon), at most 22% of coverage will be lost inthe extreme case. Observe that the additional coverage will bethe largest when X is located on some boundary of the poly-gon. Let A and B be the two end points of that boundary. Wesee that the additional coverage will be the largest if A andB are each separated from X by the transmission range r asillustrated in figure 6(c). It is not hard to find that the size ofthe shaded area in figure 6(c) is

4

[∫ r/2

0

√r2 − x2 dx −

∫ r

r/2

√r2 − x2 dx

]

=(√

3 − π

3

)r2 ≈ 0.22πr2.

Now it is reasonable to say that if a host is in the convexpolygon formed by the locations of previously sending hosts,the additional coverage that the host can provide is well be-low 22%.

3.5. Cluster-based scheme

The above schemes are based on statistical and geometricmodeling to estimate the additional coverage of a rebroadcast.In the following, we show how to develop an approach basedon graph modeling. Specifically, we will adopt the cluster-ing concept of [2,16] to derive our scheme. Note that theclustering technique has been used to solve other problems


Figure 7. A MANET with three clusters.

in MANETs (e.g., traffic coordination [14], routing [14], andfault-tolerance [1]).

We first summarize the cluster formation algorithm pro-posed in [16]. It is assumed that a host periodically sendspackets to advertise its presence. Thus any host can deter-mine its connectivity with other hosts on its own. Each hosthas a unique ID. A cluster is a set of hosts formed as follows.A host with a local minimal ID will elect itself as a clusterhead. This head host together with its neighbors will form acluster. These neighbor hosts are called members of the clus-ter. Within a cluster, a member that can communicate witha host in another cluster is a gateway. To take mobility intoaccount, when two heads meet, the one with a larger ID givesup its head role. Figure 7 shows an example of a clusteredMANET.

Now back to our broadcast storm problem. We assumethat clusters have been formed in the MANET and will bemaintained regularly by the underlying cluster formation pro-tocol. In a cluster, the head’s rebroadcast can cover all otherhosts in that cluster if its transmission experiences no colli-sion. Apparently, to propagate the broadcast message to hostsin other clusters, gateway hosts should take the responsibility.But there is no need for a non-gateway member to rebroadcastthe message. Based on these observations, our cluster-basedscheme is formally developed as follows.

S1. When a broadcast message msg is heard for the first time,if the host is a non-gateway member, the rebroadcast isprohibited and the procedure exits. Otherwise, the host iseither a head or a gateway. Proceed to S2.

S2. Use any of the probabilistic, counter-based, distance-based, and location-based schemes to determine whetherto rebroadcast or not.

Note that the above scheme is derived by incorporating anyof the schemes developed earlier into it. Step S1 is to preventnon-gateway members from rebroadcasting. As a cluster maystill have many gateway members, step S2 then further uti-lizes other knowledge (such as additional coverage) to reducethe number of rebroadcasts.

4. Performance simulation

We have developed a simulator using C++. Central to thesimulator is a discrete event-driven engine designed to simu-late systems that can be modeled by processes communicating

through signals. A simplified version of the MAC specifica-tion in IEEE 801.11 is referenced to simulate the CSMA/CAbehavior among hosts.

The fixed parameters in our simulations are the transmis-sion radius (500 m), the broadcast packet size (280 bytes), thetransmission rate (1M bits per second), and the DSSS physi-cal layer timing (PLCP overhead, slot time, inter-frame sepa-rations, backoff window sizing, as suggested in IEEE 801.11).

A geometric area called a map which contains 100 mobilehosts is simulated. A map can be of size 1 × 1, 3 × 3, 5 × 5,7 × 7, 9 × 9, or 11 × 11 units, where a unit is of length 500 m(the transmission radius). Initially, hosts are randomly dis-tributed over a map. To simulate host mobility, each host willroam around randomly in the map during the simulation. Theroaming pattern of a host is simulated by generating a seriesof turns. In each turn, a direction, a velocity, and a time in-terval are generated. The direction is uniformly distributedfrom 0◦ to 360◦, and the time interval uniformly distributedfrom 1 to 100 s. The velocity is randomly chosen from 0 to10 km/h in a 1 × 1 map, and similarly from 0 to 30 km/hin a 3 × 3 map, from 0 to 50 km/h in a 5 × 5 map, from0 to 90 km/h in a 9 × 9 map, and from 0 to 110 km/h in a11 × 11 map, respectively. The arrival rate for the whole mapis one broadcast request per second, and the broadcasting hostis randomly picked for each request. Recall that we need todifferentiate the timing of rebroadcasting. A small randomdelay ranging from 0 to 31 slots is inserted before each at-tempt at rebroadcasting (for instance, refer to step S2 of thecounter-based scheme in section 3.2).

The performance metrics to be observed are:

• REachability (RE): the number of mobile hosts receivingthe broadcast message divided by the total number of mo-bile hosts that are reachable,4 directly or indirectly, fromthe source host.

• Saved ReBroadcast (SRB): (r − t)/r , where r is the num-ber of hosts receiving the broadcast message, and t is thenumber of hosts that actually transmitted the message.

• Average latency: the interval from the time the broadcastwas initiated to the time the last host finished its rebroad-casting.

4.1. Performance of the probabilistic, counter-based,distance-based, location-based, and cluster-basedschemes

The simulation results of the probabilistic, counter-based,distance-based, location-based, and cluster-based schemesare shown in figures 8, 9, 10, 11, and 12, respectively. Eachvalue in these figures is obtained through a simulation run of10,000 broadcast requests.

Figure 8(a) shows the observed RE and SRB when apply-ing the probabilistic scheme. In a small map (which impliesa dense host distribution), a small probability P is sufficientto achieve high reachability. But a larger P is needed if the

4 This is to take the network partitioning problem into account.

160 TSENG ET AL.

Figure 8. Performance of the probabilistic scheme. (a) Probability P versus reachability RE (shown in lines) and saved rebroadcast SRB (shown in bars).(b) Probability P versus average latency.

Figure 9. Performance of the counter-based scheme. (a) Counter threshold C versus reachability RE (shown in lines) and saved rebroadcast SRB (shown inbars). (b) Counter threshold C versus average latency.

host distribution is sparse. The amount of saving (SRB) de-creases, roughly proportionally to (1 − P), as P increases.Also, note that the performance of broadcasting by floodingcan be found at the position where the probability P = 1.Figure 8(b) shows the broadcast latency at various P values.Interestingly, a MANET with sparser hosts tends to completebroadcasting more quickly than one with denser hosts. Thereason is probably due to the heavier contention on the chan-nel in denser networks.

Figure 9 shows the performance of the counter-basedscheme. From figure 9(a), we see that the reachability RE infact reaches about the same level when the counter thresholdC � 3. Note that when C is large enough (such as C = 6),the network’s behavior is very close to one using flooding tobroadcast. However, various levels of SRB can be obtainedover the flooding scheme, depending on the density of hostsin a map. For instance, in denser maps (e.g., 1 × 1, 3 × 3,and 5 × 5) this scheme can offer 27–67% of SRB at C = 3,whereas in sparser maps (e.g., 7 × 7, 9 × 9, and 11 × 11) the

scheme can offer 8–20% of SRB. When the map is very sparse(say 11 × 11) and C is very large (say 6), the amount of sav-ing decreases sharply, because the number of neighbors of ahost becomes very small (2.4 neighbors per host in an 11×11map in average). Under such conditions, it is less likely thata host will receive the same broadcast message more than C

times. We recommend that a threshold C of 3 or 4 is probablya reasonable choice.

The performance of the distance-based scheme is shown infigure 10. Note that the values of threshold D in the figure arechosen purposely to make a reasonable comparison betweenthe distance-based scheme and the counter-based scheme. Forinstance, when C = 2, we know that EAC(2) ≈ 0.187 fromfigure 4. So we choose a D to match this value of additioncoverage, i.e., (πr2−INTC(D))/(πr2) ≈ 0.187. This gives aD = 147. The other D values along the x-axis in figure 10 arederived in a similar way. Note that when D is small enough(such as D = 11), the network’s behavior is very close to oneusing flooding to broadcast.


Figure 10. Performance of the distance-based scheme. (a) Distance threshold D versus reachability RE (shown in lines) and saved rebroadcast SRB (shownin bars). (b) Distance threshold D versus average latency.

Figure 11. Performance of the location-based scheme. (a) Coverage threshold A versus reachability RE (shown in lines) and saved rebroadcast SRB (shownin bars). (b) Coverage threshold A versus average latency.

By comparing figures 9(a) and 10(a), we observe that thedistance-based scheme can provide better reachability. Butnot many rebroadcasts are saved – its SRB values are worsethan those of the counter-based scheme. Also, its broad-cast latency is higher than that of the counter-based scheme,as shown in figures 9(b) and 10(b). The reason that thedistance-based scheme saves less among of rebroadcasts thanthe counter-based scheme is as follows. In the distance-based scheme, a host may have heard a broadcast messagemany times but still decide to rebroadcast the message be-cause none of the transmission distances are below the dis-tance threshold D, whereas the rebroadcast may be canceledif the counter-based scheme is used.

Figure 11 illustrates the performance of the location-basedscheme at various threshold values of A. Note that the valuesof A in the figure are chosen purposely to make a fair com-parison with the distance-based and counter-based schemes(recall the earlier discussion about how to choose the values

of D in figure 10). Clearly, as compared to the counter-basedand distance-based schemes, the location-based scheme hasthe best reachability RE and the best saving SRB. Becauseof the saving, the broadcast latency is also the best among allschemes. The reason is that the location-based scheme usesthe most accurate information (i.e., locations of the transmit-ting hosts) to determine the additional coverage of a rebroad-cast.

Figure 12 shows the performance of the cluster-basedscheme where the location-based scheme is incorporated in itsstep S2. Compared to the original location-based scheme, thecluster-based scheme apparently saves much more rebroad-casts and leads to shorter average broadcast latencies. Un-fortunately, the reachability is unacceptable at sparser areas(such as 7 × 7, 9 × 9, and 11 × 11 maps). This is prob-ably because when the number of hosts participating in re-broadcasting is reduced (mostly by its step S1), the collisionscaused by the hidden terminal problem will significantly re-

162 TSENG ET AL.

Figure 12. Performance of the cluster-based scheme by applying the location-based scheme in its step S2. (a) The coverage threshold A versus reachabilityRE (shown in lines) and saved rebroadcast SRB (shown in bars). (b) The coverage threshold A versus average latency.

Figure 13. An example of the hidden terminal problem in the cluster-basedscheme. Hosts A, C, and D are gateways, B and E are cluster heads.

duce the chance of successful transmissions. For example,figure 13 illustrates an interesting scenario where a broadcastpacket was propagated through gateway A to head B. After Brebroadcasts the message, gateways C and D will try to re-broadcast the message. Unfortunately, because C and D cannot hear each other, the two rebroadcasts from C and D arevery likely to collide at E. Such problems of message cor-ruption may significantly reduce the reachability, especiallyin sparser maps.

It is also worth mentioning that in figure 12(b) there is ananomaly that the broadcast latencies in a 1×1 map are partic-ularly low. This is because in our simulations there were onaverage only 1.2 cluster heads. When there is only one head,the broadcast will be completed very quickly because there isno gateway.

4.2. The relationship between RE and SRB

In the previous section, we have shown the reachability(RE) and the saved rebroadcasts (SRB) offered by differentschemes at different threshold values. Apparently, it is desir-able to have high RE as well as high SRB. In this section, wetry to establish the relationship between these two metrics.In addition, we also provide a comparison among differentschemes.

In figure 14, we show the RE and SRB offered by thecounter-based, distance-based, and location-based schemes atdifferent threshold values in various map sizes. These data areredrawn from figures 9(a), 10(a), and 11(a) through x–y scat-ter graphs. The x-axis indicates the RE and the y-axis indi-cates the SRB. So the parts closer to the upper-right cornersof these graphs are more desirable.

Figure 14(a) shows that in a 1 × 1 map, both RE and SRBapproach toward the upper-right corner of the graph as a “lessstrict” threshold value is used in each of these three schemes(“less strict” in the sense that it is easier to satisfy the condi-tion to prohibit a broadcast packet from being rebroadcast).For instance, C = 2 is less strict than C = 3, D = 147 isless strict than D = 72, and A = 0.187 is less strict thanA = 0.09. Since a 1 × 1 map indicates a more crowded sce-nario, this actually justifies the severity of the broadcast stormproblem when mobile hosts are close to each other, and thus,reducing the possibility of rebroadcasting is very important.Overall, the location-based scheme with A = 0.187 is thebest choice.

Figure 14(b) shows the same simulation results in a3 × 3 map. The least strict threshold values (C = 2, D = 147,and A = 0.187) no longer offer the highest RE now. However,the RE’s offered by all schemes at all threshold values areall pretty satisfactory (all above 99%). So one could choosea scheme that offers the best SRB. Overall, the counter-based scheme with C = 2 and the location-based scheme withA = 0.187 are better choices.

Figures 14(c)–(f) show the results in larger maps. Gen-erally speaking, in all three schemes, the relationship be-tween RE and SRB shows a tradeoff: a higher RE will leadto a lower SRB, and vice versa. Thus, it really depends onwhether one would emphasize RE or SRB when choosingan appropriate threshold value. As to the performance ofthese three schemes, the location-based scheme is still thebest choice as its curve is closest to the upper-right cornersof these graphs.


Figure 14. The reachability (RE) and save rebroadcast (SRB) offered by the probabilistic, counter-based, distance-based, and location-based schemes atdifferent threshold values in various map sizes. These schemes are denoted by P, C, D, and A, respectively. The values appended to P, C, D, and A are the

corresponding threshold values.

164 TSENG ET AL.

Figure 15. The effect of load on RE and SRB in a 1×1: (a) probabilistic scheme, (b) counter-based scheme, (c) distance-based scheme, and (d) location-basedscheme.

To conclude this section, observe that in each graph in fig-ure 14, there is a point “P1”, which indicates the RE andSRB offered by the probabilistic scheme with P = 1 (i.e.,the flooding scheme, so its SRB = 0). As can be seen, ex-cept in a 5 × 5 map, it is always beneficial to adopt our pro-posed schemes as one can always find a scheme which offersa higher RE with a non-zero SRB.

4.3. The effect of load

In all previous simulations, we have used an arrival rate ofone broadcast request per second. We have also increased thearrival rates to 10, 20, 30, 40, and 50 broadcast requests persecond to observe the effect. Four schemes, the probabilistic,counter-based, distance-based, and location-based schemes,were tested. The simulation results are shown in figures 15,16, and 17 for maps of sizes 1×1, 5×5, and 9×9, respectively.Note that the value appended at each scheme indicates thethreshold value used.

From these figures, we make three observations. First, wesee an interesting fact that a heavier load will result in a lowerreachability. This is true for all schemes, because a heav-

ier load actually means more contention and collision amongbroadcast packets. The effect is more serious in denser mapsthan in sparser maps. We believe that this can justify theseverity of the broadcast storm identified in this paper.

Second, to increase the reachability RE, a less strict thresh-old value should be used in smaller maps, whereas a stricterthreshold value should be used in larger maps. Takingthe counter-based scheme as an example, in figure 15(b), athreshold C = 2 will offer the highest RE in a 1 × 1 map.When a larger 5 × 5 map is considered, figure 16(b) showsthat the counter-based scheme with C = 2 is no longer thebest when the load is below 20 broadcast requests per second.However, it still has its advantage when the load is above 20.Moving to a much larger 9 × 9 map, figure 17(b) shows thatC = 2 will offer the lowest RE. The same scenario can beobserved in other schemes too. This observation also impliesthat a larger map can distribute the broadcast requests to largerphysical areas, and thus lower the severity of contention andcollision caused by rebroadcasting.

Third, we observe that for all schemes, the SRB will de-crease as the load increases in a 1 × 1 map. This trend is



very unfavorable because this means that there are more hoststrying to help rebroadcast the broadcast packets, but the levelof RE keeps on going down as load increases. Fortunately,in larger maps, such as 5 × 5 and 9 × 9, the SRB remainsalmost unchanged as load increases. This again justifies thatthe more crowded the area is, the more serious the broadcaststorm is.

5. Conclusions

This paper has identified an important issue in a MANET,the broadcast storm problem. We have demonstrated, throughanalyses and simulations, how serious this problem couldbe. Several schemes, namely probabilistic, counter-based,distance-based, location-based, and cluster-based schemes,have been proposed to alleviate this problem. Simulation re-sults based on different threshold values are presented to ver-ify and compare the effectiveness of these schemes. As com-pared to the basic flooding approach, a simple counter-basedscheme can eliminate many redundant rebroadcasts when

the host distribution is dense. The distance-based schemehas higher reachability than the counter-based scheme, butthe amount of saving (in terms of the number of saved re-broadcasts) is not satisfactory. Among all, the location-basedscheme is the best choice because it can eliminate most redun-dant rebroadcasts under all kinds of host distributions withoutcompromising reachability.

Acknowledgements

This research is supported in part by the Ministry of Ed-ucation of R.O.C. under grant 89-H-FA07-1-4 (LearningTechnology) and the National Science Council of R.O.C.under grants NSC89-2218-E-009-093, NSC89-2218-E-009-094, and NSC89-2218-E-009-095. A preliminary version ofthis paper has appeared in the International Conference onMobile Computing and Networking, 1999 (MobiCom’99).

The authors would like to thank the anonymous refereesfor carefully reading an earlier version of this paper and giv-ing many helpful suggestions.

166 TSENG ET AL.


References

[1] S. Alagar, S. Venkatesan and J. Cleveland, Reliable broadcast in mo-bile wireless networks, in: Military Communications Conference (MIL-COM), Conference Record, Vol. 1 (1995) pp. 236–240.

[2] D.J. Baker and A. Ephremides, The architecture organization of a mo-bile radio network via a distributed algorithm, IEEE Transactions onCommunications COM-29(11) (November 1981) 1694–1701.

[3] R. Bar-Yehuda, O. Goldreich and A. Itai, Efficient emulation of single-hop radio network with collision detection on multi-hop radio networkwith no collision detection, Distributed Computing 5(2) (1991) 67–72.

[4] R. Bar-Yehuda, O. Goldreich and A. Itai, On the time-complexity ofbroadcast in multi-hop radio networks: An exponential gap betweendeterminism and randomization, Journal of Computer and System Sci-ences 45(1) (August 1992) 104–126.

[5] R. Bar-Yehuda, A. Israeli and A. Itai, Multiple communication in multi-hop radio networks, SIAM Journal on Computing 22(4) (August 1993)875–887.

[6] S. Basagni, I. Chlamtac, V.R. Syrotiuk and B.A. Woodward, A distancerouting effect algorithm for mobility (DREAM), in: Proc. IEEE/ACMInternational Conference on Mobile Computing and Networking (MO-BICOM) (1998) pp. 76–84.

[7] J. Broch, D.B. Johnson and D.A. Maltz, The dynamic source routingprotocol for mobile ad hoc networks, Internet Draft (work in progess)(1998) draft-ietf-manet-dsr-00.txt

[8] J. Broch, D.A. Maltz, D.B. Johnson, Y.-C. Hu and J. Jetcheva, A perfor-mance comparison of multi-hop wireless ad hoc network routing pro-tocols, in: Proc. IEEE/ACM Interference Conference on Mobile Com-puting and Networking (MOBICOM) (1998) pp. 85–97.

[9] I. Chlamtac and A. Faragó, Making transmission schedules immuneto topology changes in multi-hop packet radio networks, IEEE/ACMTransactions on Networking 2(1) (February 1994) 23–29.

[10] I. Chlamtac and S. Kutten, On broadcasting in radio networks – prob-lem analysis and protocol design, IEEE Transactions on Communica-tions COM-33(12) (December 1985) 1240–1246.

[11] I. Chlamtac and S. Kutten, Tree-based broadcasting in multihop radionetworks, IEEE Transactions on Communications COM-36(10) (Octo-ber 1987) 1209–1223.

[12] I. Chlamtac and O. Weinstein, The wave expansion approach to broad-casting in multihop radio networks, IEEE Transactions on Communi-cations COM-39(3) (March 1991) 426–433.

[13] R. Dube, C.D. Rais, K.-Y. Wang and S.K. Tripathi, Signal stabilitybased adaptive routing (SSA) for ad-hoc mobile networks, Technicalreport CS-TR-3646, University of Maryland, College Park (August1996).

[14] M. Gerla and J.T.-C. Tsai, Multicluster, mobile, multimedia radio net-work, Wireless Networks 1(3) (1995) 255–265.

[15] Z.J. Haas and M.R. Pearlman, The zone routing protocol (ZRP)for ad hoc networks, Internet Draft (work in progess) (1998)draft-ietf-manet-zone-zrp-01.txt


[16] M. Jiang, J. Li and Y.C. Tay, Cluster based routing protocol (CBRP)functional specification, Internet Draft (work in progess) (1998)draft-ietf-manet-cbrp-spec-00.txt

[17] J.-H. Ju and V.O.K. Li, An optimal topology-transparent schedulingmethod in multihop packet radio networks, IEEE/ACM Transactionson Networking 6(3) (June 1998) 298–306.

[18] E.D. Kaplan (ed.), Understanding GPS: Principles and Applications(Artech House, Boston, MA, 1996).

[19] Y.-B. Ko and N.H. Vaidya, Location-aided routing (LAR) in mobilead hoc networks, in: Proc. IEEE/ACM International Conference onMobile Computing and Networking (MOBICOM) (1998) pp. 66–75.

[20] LAN MAN Standards Committee of the IEEE Computer Society, IEEEStandard 802.11-1997, Wireless LAN Medium Access Control (MAC)and Physical Layer (PHY) specifications (November 1997).

[21] C. Lee, J.E. Burns and M.H. Ammar, Improved randomized broad-cast protocol in multi-hop radio networks, Technical report GIT-CC-93-14, College of Computing, Georgia Institute of Technology (February1993).

[22] J.P. Macker and M.S. Corson, Mobile ad hoc networking and theIETF, ACM Mobile Computing and Communications Review 2(3)(July 1998) 7–9.

[23] A. Michail and A. Ephremides, Energy efficient routing for connection-oriented traffic in ad-hoc wireless networks, in: IEEE InternationalSymposium on Personal, Indoor and Mobile Radio Communications(2000) pp. 762–766.

[24] E. Pagani and G.P. Rossi, Reliable broadcast in mobile multihop packetnetworks, in: Proc. IEEE/ACM International Conference on MobileComputing and Networking (MOBICOM) (1997) pp. 34–42.

[25] C.E. Perkins and E.M. Royer, Ad hoc on demand distance vec-tor (AODV) routing, Internet Draft (work in progess) (1998)draft-ietf-manet-aodv-02.txt

[26] S. Ramanathan and E.L. Lloyd, Scheduling algorithms for multihopradio networks, IEEE/ACM Transactions on Networking 2(2) (April1993) 166–177.

[27] A. Sen and M.L. Huson, A new model for scheduling packet radio net-works, Wireless Networks 3 (1997) 71–82.

[28] E.K. Wesel, Wireless Multimedia Communications: Networking Video,Voice, and Data (Addison-Wesley, Reading, MA, 1998).

Yu-Chee Tseng received his B.S. and M.S. degreesin computer science from the National Taiwan Uni-versity and the National Tsing-Hua University in1985 and 1987, respectively. He worked for theD-LINK Inc. as an engineer in 1990. He obtainedhis Ph.D. in computer and information science fromthe Ohio State University, in January of 1994. From1994 to 1996, he was an Associate Professor at theDepartment of Computer Science, Chung-Hua Uni-versity. He joined the Department of Computer Sci-

ence and Information Engineering, National Central University, in 1996 andhas become a Full Professor since 1999. Since August 2000, he has becomea Full Professor at the Department of Computer Science and Information En-gineering, National Chiao-Tung University, Taiwan. Dr. Tseng served as aProgram Committee Member in the International Conference on Parallel andDistributed Systems 1996, the International Conference on Parallel Process-ing 1998, the International Conference on Distributed Computing Systems2000, and the International Conference on Computer Communications andNetworks 2000. He was the Chair of the first and second Wireless Networksand Mobile Computing Workshop in 2000 and 2001. His research interestsinclude wireless communication, network security, parallel and distributed

computing, and computer architecture. Dr. Tseng is a member of the IEEEComputer Society and the Association for Computing Machinery.

E-mail: [email protected]

Sze-Yao Ni received his BS and MS degrees in elec-trical engineering from the National Central Univer-sity, Taiwan, Republic of China, in 1991 and 1993,respectively. He received his PhD in computer sci-ence and information engineering from the NationalCentral University in 2000. His research interests in-clude computer architecture, parallel and distributedcomputing, and mobile computing. Mr. Ni is a mem-ber of the IEEE, the ACM, and the Phi Tau Phi Soci-ety.

Yuh-Shyan Chen received the B.S. degree in com-puter science from Tamkang University, Taiwan, Re-public of China, in June 1988 and the M.S. and Ph.D.degrees in computer science and information engi-neering from the National Central University, Tai-wan, Republic of China, in June 1991 and January1996, respectively. He joined the faculty of Depart-ment of Computer Science and Information Engi-neering at Chung-Hua University, Taiwan, Repub-lic of China, as an Associate Professor in February

1996. He joined the Department of Statistic, National Taipei University,in August 2000. Dr. Chen served as a Program Committee Member in theInternational Conference on Parallel and Distributed Systems 2001, the In-ternational Conference on Computer Communications and Networks 2001.He was a Workshop Co-Chair of the 2001 Mobile Computing Workshop,and Guest Editor of Journal of Internet Technology, special issue on ”Wire-less Internet Applications and Systems”. His paper wins the 2001 IEEE15th ICOIN-15 Best Paper Award. His current research includes paral-lel/distributed processing, network security, wireless communication/mobilecomputing, and ad hoc QoS routing protocols. Dr. Chen is a member of theIEEE Computer Society and Phi Tau Phi Society.

Jang-Ping Sheu received the B.S. degree in com-puter science from Tamkang University, Taiwan, Re-public of China, in 1981, and the M.S. and Ph.D.degrees in computer science from the National TsingHua University, Taiwan, Republic of China, in 1983and 1987, respectively. He joined the faculty of theDepartment of Electrical Engineering, National Cen-tral University, Taiwan, Republic of China, as an As-sociate Professor in 1987. He is currently a Professorof the Department of Computer Science and Infor-

mation Engineering, National Central University. He was a Chair of Depart-ment of Computer Science and Information Engineering, National CentralUniversity from August 1997 to July 1999. He was a Visiting Professor atthe Department of Electrical and Computer Engineering, University of Cal-ifornia, Irvine from July 1999 to April 2000. His current research interestsinclude parallel processing and mobile computing. Dr. Sheu is a senior mem-ber of the IEEE, a member of the ACM and Phi Tau Phi Society. He isan Associate Editor of Journal of Information Science and Engineering andJournal of the Chinese Institute of Engineers. He received the DistinguishedResearch Awards of the National Science Council of the Republic of Chinain 1993–1994, 1995–1996, and 1997–1998.

the broadcast storm problem in a mobile ad hoc...

Documents